Kandinsky 6.0 generates five seconds of video with its audio, voices included, from a text prompt (MIT license). I wanted to know two things: does it run on a 16 GB card, and can it speak Spanish?
Memory. On a rented 16 GB RTX 4080, the README recipe (per-component CPU offload) ran out of memory with the distilled Lite model; the Qwen2.5-VL text encoder files alone add up to 15.45 GiB. An 8-bit text encoder and sequential offload failed too. What worked was a two-phase recipe: compute the prompt embeddings first, streaming the encoder through the GPU in groups of layers, then drop it and generate with a tiled video VAE. PyTorch allocated at most 11.24 GiB; a vertical clip took about 141 s; the process reached 45.9 GiB of system RAM.
Speech. Eight scenes with one Spanish line each, two seeds, scored with faster-whisper large-v3 against the requested line. The rule, written before running: median word error ≤ 0.20 and Spanish detected in ≥ 12 of 16 clips.
| Model | Median Spanish WER | Clips with WER ≤ 0.20 |
|---|---|---|
| Lite distilled (3B, 10 steps) | 1.06 | 1 of 16 |
| Lite undistilled (50 steps, guidance 5) | 0.75 | 1 of 16 |
| Pro distilled (29B, 10 steps) | 0.15 | 9 of 16 |
Only Pro met the rule, and its process reached 120 GiB of RAM at roughly 12 to 23 minutes per clip (RTX 3090 with PyTorch memory capped at 15.57 GiB, transformer streamed block by block). The data is compatible with an advantage of the bigger model, but it cannot separate size from distillation.
The full write-up has the recipes, the component diagram, clips with what Whisper heard, the threats to validity (three external audits), and a demo: the small model's video with a replaced voice and the mouth redrawn by LatentSync 1.6.
Read the full measurements → https://efraingaray.com/en/blog/kandinsky6-video-audio-16gb/
Scripts, the pre-registration sheet and the raw JSONL: gist.
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